{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要的工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据\n",
    "df = pd.read_csv('./pima-indians-diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAE6VJREFUeJzt3X+wJWV95/H3ZwAJ6w8G5EKRmSEDcTarFSPBkaWKlEnAZAU3DCGgSZGIOruTrYIUCdls0BipDSmjJmqWNWsyu4iDUQkSlBFRZEfQyg+VQZQfQYuRIEyGZUZFfsYfI9/94zxXjpeee3uYOfcc7n2/qk6d7qefPud7qFvzofvpfjpVhSRJMy0ZdwGSpMlkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6rTvuAvYE4ccckitXLly3GVI0tPKTTfd9PWqmpqr39M6IFauXMnmzZvHXYYkPa0k+Vqffp5ikiR1MiAkSZ0MCElSJwNCktTJgJAkdRppQCS5O8mtSb6YZHNrOzjJdUnubO8HtfYkuSjJliS3JDlmlLVJkmY3H0cQP19VR1fV6rZ+PrCpqlYBm9o6wEnAqvZaB7x7HmqTJO3COE4xrQE2tOUNwKlD7ZfWwGeBpUkOH0N9kiRGHxAFfDLJTUnWtbbDquo+gPZ+aGtfBtw7tO/W1iZJGoNR30l9fFVtS3IocF2SL8/SNx1t9aROg6BZB3DEEUfscYEv/r1L9/gztPDc9KevHncJ0tiN9Aiiqra19+3Ah4FjgfunTx219+2t+1ZgxdDuy4FtHZ+5vqpWV9Xqqak5pxKRJD1FIwuIJM9M8uzpZeAXgduAjcBZrdtZwFVteSPw6nY103HAg9OnoiRJ82+Up5gOAz6cZPp7PlBVn0hyI3B5krXAPcAZrf81wMnAFuAx4LUjrE2SNIeRBURV3QW8qKP9G8CJHe0FnD2qeiRJu8c7qSVJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdRp5QCTZJ8nNSa5u60cm+VySO5P8TZJntPb92/qWtn3lqGuTJO3afBxBnAvcMbT+VuCdVbUKeABY29rXAg9U1fOAd7Z+kqQxGWlAJFkOvAL4P209wAnAFa3LBuDUtrymrdO2n9j6S5LGYNRHEH8O/Dfg8bb+XOBbVbWzrW8FlrXlZcC9AG37g62/JGkMRhYQSf4jsL2qbhpu7uhaPbYNf+66JJuTbN6xY8deqFSS1GWURxDHA6ckuRu4jMGppT8HlibZt/VZDmxry1uBFQBt+4HAN2d+aFWtr6rVVbV6ampqhOVL0uK2WwGR5KAkP9Wnb1W9vqqWV9VK4FeBT1XVmcD1wOmt21nAVW15Y1unbf9UVT3pCEKSND/mDIgkNyR5TpKDgS8BlyR5xx585+8D5yXZwmCM4eLWfjHw3NZ+HnD+HnyHJGkP7Tt3Fw6sqoeS/Cfgkqq6IMktu/MlVXUDcENbvgs4tqPPt4EzdudzJUmj0+cU075JDgdeCVw94nokSROiT0D8EXAt8NWqujHJUcCdoy1LkjRuc55iqqoPAR8aWr8L+JVRFiVJGr8+g9T/NsmmJLe19Z9K8sbRlyZJGqc+p5j+N/B64HsAVXULg8tWJUkLWJ+A+DdV9fkZbTs7e0qSFow+AfH1JD9Om/YiyenAfSOtSpI0dn3ugzgbWA/8uyT/Avwz8OsjrUqSNHZ9rmK6C3hZkmcCS6rq4dGXJUkatz5XMb05ydKqerSqHm7zMf3xfBQnSRqfPmMQJ1XVt6ZXquoB4OTRlSRJmgR9AmKfJPtPryQ5ANh/lv6SpAWgzyD1XwObklzC4Eqm1/HEo0ElSQtUn0HqtyW5FTiRwVPfLqyqa0demSRprPocQVBVHwc+PuJaJEkTpM9VTKcluTPJg0keSvJwkofmozhJ0vj0OYJ4G/BLVXXHqIuRJE2OPlcx3W84SNLi0+cIYnOSvwE+AnxnurGqrhxZVZKksesTEM8BHgN+caitAANCkhawPpe5vnY+CpEkTRafKCdJ6uQT5SRJnXyinCSpk0+UkyR1eqpPlDtzpFVJksZu1oBIsgRYXVU+UU6SFplZTzFV1ePAOW35UcNBkhaPPmMQ1yX5r0lWJDl4+jXyyiRJY9VnDOJ17f3sobYCjtr75UiSJkWfO6mPnI9CJEmTZc6ASPLqrvaqunTvlyNJmhR9TjG9ZGj5Rxg8evQLgAEhSQtYn1NMvzW8nuRA4H1z7ZfkR4DPAPu377miqi5IciRwGXAwg6D5jar6bpL9GYTOi4FvAK+qqrt37+dIkvaWPlcxzfQYsKpHv+8AJ1TVi4CjgZcnOQ54K/DOqloFPACsbf3XAg9U1fOAd7Z+kqQx6TOb60eTbGyvq4GvAFfNtV8NPNJW92uvAk4ArmjtG4BT2/Katk7bfmKS9P4lkqS9qs8YxJ8NLe8EvlZVW/t8eJJ9gJuA5wF/AXwV+FZVTU/2txVY1paXAfcCVNXOJA8CzwW+3ue7JEl7V5+AuAe4r6q+DZDkgCQr+4wPVNX3gaOTLAU+DDy/q1t77zpaqJkNSdYB6wCOOOKIHuVLkp6KPmMQHwIeH1r/fmvrraq+BdwAHAcsTTIdTMuBbW15K7ACoG0/EPhmx2etr6rVVbV6ampqd8qQJO2GPgGxb1V9d3qlLT9jrp2STLUjB5IcALwMuAO4Hji9dTuLJ8YzNrZ12vZPVdWTjiAkSfOjzymmHUlOqaqNAEnW0G9c4HBgQxuHWAJcXlVXJ/kn4LIkfwzcDFzc+l8MvC/JFgZHDj61TpLGqE9A/Bfg/Une1da3Ap13Vw9rjyb96Y72u4BjO9q/DZzRox5J0jzoc6PcV4HjkjwLiFN+S9Li0Oc+iDcnWVpVj1TVw0kOaqeHJEkLWJ9B6pPaVUgAVNUDwMmjK0mSNAn6BMQ+bZ4k4AdXJO0/S39J0gLQZ5D6r4FNSS5hcOPa63hiSgxJ0gLVZ5D6bUluYXAfA8CFVXXtaMuSJI1bnyMIGNyvMD3Z3s2jK0eSNCn6XMX0SuDzDO5ufiXwuSSnz76XJOnprs8RxB8AL6mq7TCYQgP4vzwxZbekEbjnj1447hI0gY54063z9l19rmJaMh0OzTd67idJehrrcwTxiSTXAh9s668CrhldSZKkSdDnKqbfS3Ia8DMMntmwvqo+PPLKJElj1esqpqq6ErhyxLVIkiaIYwmSpE4GhCSp0y4DIsmm9v7W+StHkjQpZhuDODzJzwKnJLmMwQD1D1TVF0ZamSRprGYLiDcB5wPLgXfM2FbACaMqSpI0frsMiKq6ArgiyR9W1YXzWJMkaQL0uQ/iwiSnAC9tTTdU1dWjLUuSNG59Juv7E+Bc4J/a69zWJklawPrcKPcK4OiqehwgyQYGU36/fpSFSZLGq+99EEuHlg8cRSGSpMnS5wjiT4Cbk1zP4FLXl+LRgyQteH0GqT+Y5AbgJQwC4ver6v+NujBJ0nj1nazvPmDjiGuRJE0Q52KSJHUyICRJnWYNiCRLktw2X8VIkibHrAHR7n34UpIj5qkeSdKE6DNIfThwe5LPA49ON1bVKSOrSpI0dn0C4r+PvApJ0sSZc5C6qj4N3A3s15ZvBOZ8FkSSFUmuT3JHktuTnNvaD05yXZI72/tBrT1JLkqyJcktSY7Zo18mSdojfSbr+8/AFcBftaZlwEd6fPZO4Her6vnAccDZSV7A4BkTm6pqFbCprQOcBKxqr3XAu3fjd0iS9rI+l7meDRwPPARQVXcCh861U1XdN/3Uuap6GLiDQbisATa0bhuAU9vyGuDSGvgssDTJ4bvxWyRJe1GfgPhOVX13eiXJvgyeKNdbkpXATwOfAw5rd2ZP36E9HTbLgHuHdtva2iRJY9AnID6d5A3AAUl+AfgQ8NG+X5DkWcDfAr9dVQ/N1rWj7UlBlGRdks1JNu/YsaNvGZKk3dQnIM4HdgC3Ar8JXAO8sc+HJ9mPQTi8v6qubM33T586au/bW/tWYMXQ7suBbTM/s6rWV9Xqqlo9NTXVpwxJ0lPQZzbXx9tDgj7H4P/ov1JVc55iShLgYuCOqnrH0KaNwFnAW9r7VUPt5yS5DPj3wIPTp6IkSfNvzoBI8grgL4GvMjgNdGSS36yqj8+x6/HAbwC3Jvlia3sDg2C4PMla4B7gjLbtGuBkYAvwGPDa3fwtkqS9qM+Ncm8Hfr6qtgAk+XHgY8CsAVFVf0f3uALAiR39i8EVU5KkCdBnDGL7dDg0d/HEuIEkaYHa5RFEktPa4u1JrgEuZzAGcQaDu6klSQvYbKeYfmlo+X7gZ9vyDuCgkVUkSZoIuwyIqnKQWJIWsT5XMR0J/Bawcri/031L0sLW5yqmjzC4n+GjwOOjLUeSNCn6BMS3q+qikVciSZoofQLifyS5APgk8J3pxumZWiVJC1OfgHghgzuiT+CJU0zV1iVJC1SfgPhl4KjhKb8lSQtfnzupvwQsHXUhkqTJ0ucI4jDgy0lu5IfHILzMVZIWsD4BccHIq5AkTZw+z4P49HwUIkmaLH3upH6YJx79+QxgP+DRqnrOKAuTJI1XnyOIZw+vJzkVOHZkFUmSJkKfq5h+SFV9BO+BkKQFr88pptOGVpcAq3nilJMkaYHqcxXT8HMhdgJ3A2tGUo0kaWL0GYPwuRCStAjN9sjRN82yX1XVhSOoR5I0IWY7gni0o+2ZwFrguYABIUkL2GyPHH379HKSZwPnAq8FLgPevqv9JEkLw6xjEEkOBs4DzgQ2AMdU1QPzUZgkabxmG4P4U+A0YD3wwqp6ZN6qkiSN3Ww3yv0u8KPAG4FtSR5qr4eTPDQ/5UmSxmW2MYjdvstakrRwGAKSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqdPIAiLJe5JsT3LbUNvBSa5Lcmd7P6i1J8lFSbYkuSXJMaOqS5LUzyiPIN4LvHxG2/nApqpaBWxq6wAnAavaax3w7hHWJUnqYWQBUVWfAb45o3kNgzmdaO+nDrVfWgOfBZYmOXxUtUmS5jbfYxCHVdV9AO390Na+DLh3qN/W1vYkSdYl2Zxk844dO0ZarCQtZpMySJ2Ots7nXlfV+qpaXVWrp6amRlyWJC1e8x0Q90+fOmrv21v7VmDFUL/lwLZ5rk2SNGS+A2IjcFZbPgu4aqj91e1qpuOAB6dPRUmSxmPWBwbtiSQfBH4OOCTJVuAC4C3A5UnWAvcAZ7Tu1wAnA1uAxxg8uU6SNEYjC4iq+rVdbDqxo28BZ4+qFknS7puUQWpJ0oQxICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktRpogIiycuTfCXJliTnj7seSVrMJiYgkuwD/AVwEvAC4NeSvGC8VUnS4jUxAQEcC2ypqruq6rvAZcCaMdckSYvWJAXEMuDeofWtrU2SNAb7jruAIeloqyd1StYB69rqI0m+MtKqFpdDgK+Pu4hJkD87a9wl6If5tzntgq5/Knfbj/XpNEkBsRVYMbS+HNg2s1NVrQfWz1dRi0mSzVW1etx1SDP5tzkek3SK6UZgVZIjkzwD+FVg45hrkqRFa2KOIKpqZ5JzgGuBfYD3VNXtYy5LkhatiQkIgKq6Brhm3HUsYp6606Tyb3MMUvWkcWBJkiZqDEKSNEEMCDnFiSZWkvck2Z7ktnHXshgZEIucU5xowr0XePm4i1isDAg5xYkmVlV9BvjmuOtYrAwIOcWJpE4GhHpNcSJp8TEg1GuKE0mLjwEhpziR1MmAWOSqaicwPcXJHcDlTnGiSZHkg8A/Aj+RZGuSteOuaTHxTmpJUiePICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCC16Sb6f5ItJbk/ypSTnJVnStq1OctEc+78mybt28zvfsCc1S/PBy1y16CV5pKqe1ZYPBT4A/H1VXdBz/9cAq6vqnKfyndKk8ghCGlJV24F1wDkZ+LkkVwMkOTbJPyS5ub3/xNCuK5J8oj1X4wfBkuTXk3y+HaH8VZJ9krwFOKC1vX+WfvskeW+S25LcmuR35vO/hTRRz6SWJkFV3dVOMR06Y9OXgZdW1c4kLwPeDPxK23Ys8JPAY8CNST4GPAq8Cji+qr6X5H8BZ1bV+UnOqaqjAZI8v6sfcDuwrKp+svVbOsrfLc1kQEjduma5PRDYkGQVgxlv9xvadl1VfQMgyZXAzwA7gRczCAyAA4DtHZ974i76fRQ4Ksn/BD4GfHLPf5bUnwEhzZDkKOD7DP6Rfv7QpguB66vql5OsBG4Y2jZzMK8YhMyGqnr9XF+5q35JXgT8B+Bs4JXA63r/EGkPOQYhDUkyBfwl8K568hUcBwL/0pZfM2PbLyQ5OMkBwKnA3wObgNPbwDdt+4+1/t9LMn0E0tkvySHAkqr6W+APgWP22g+VevAIQmoDxgxOGe0E3ge8o6Pf2xicYjoP+NSMbX/X9nse8IGq2gyQ5I3AJ9uYxvcYHAl8DVgP3JLkC1V15i76/StwyfQlt8BcRyLSXuVlrpKkTp5ikiR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLU6f8Da+dAg7TpzbMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#标签Target的数据分布\n",
    "sns.countplot(df['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各特征的数据分析\n",
    "sns.countplot(x='pregnants',hue=df['Target'], data=df)\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Plasma_glucose_concentration'].hist()\n",
    "plt.xlabel('Plasma glucose concentration 2h after oral glucose tolerance test')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['blood_pressure'].hist()\n",
    "plt.xlabel('blood pressure')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Triceps_skin_fold_thickness'].hist()\n",
    "plt.xlabel('Triceps skin fold thickness')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGOdJREFUeJzt3X+0XWV95/H3B4JIQQn4IysC04DSUUcqQkSsrQawjoAFyoA/hhFU1qQzi7Z0xNbQdtTWjoKKP7Bd1CgqKmNEioCIUiZydVwzIiBKQFQCphChIMqv4Pgj8p0/zr7kGHZOdm5y7jn33vdrrbPO3s959j7f86yd+83ez97Pk6pCkqSNbTfqACRJ48kEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSKxOEJKmVCUKS1GreqAPYGk9+8pNr0aJFU9r24YcfZuedd962Ac0its9gts9gts9go26f66677t6qesrm6s3oBLFo0SKuvfbaKW07MTHBkiVLtm1As4jtM5jtM5jtM9io2yfJv3SpN9RLTEnWJFmV5FtJrm3Kdk9yZZJbmvfdmvIkOTvJ6iQ3JDlgmLFJkgabjj6IQ6pq/6pa3KwvA1ZW1b7AymYd4HBg3+a1FDhnGmKTJG3CKDqpjwbOa5bPA47pK/9E9XwdmJ9k4QjikyQBGeZw30l+ANwHFPChqlqe5P6qmt9X576q2i3JZcAZVfW1pnwl8OaqunajfS6ld4bBggULDlyxYsWUYlu3bh277LLLlLadC2yfwWyfwWyfwUbdPocccsh1fVd1NmnYndQvqqo7kzwVuDLJdwfUTUvZY7JXVS0HlgMsXry4ptrRM+pOonFn+wxm+wxm+ww2U9pnqJeYqurO5v0e4HPAQcDdk5eOmvd7muprgb36Nt8TuHOY8UmSNm1oCSLJzkmeMLkMvAy4EbgUOKmpdhJwSbN8KXBiczfTwcADVXXXsOKTJA02zEtMC4DPJZn8nv9ZVV9Kcg1wQZKTgduB45v6lwNHAKuBnwKvH2JskqTNGFqCqKrbgOe2lP8YOKylvIBThhWPJGnLzOgnqbfGqh8+wOuWfWEk373mjCNH8r2StCUcrE+S1MoEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSKxOEJKmVCUKS1MoEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSKxOEJKmVCUKS1MoEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSKxOEJKmVCUKS1MoEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSKxOEJKnV0BNEku2TXJ/ksmZ97yRXJ7klyWeSPK4p37FZX918vmjYsUmSNm06ziBOBW7uWz8TeF9V7QvcB5zclJ8M3FdVzwDe19STJI3IUBNEkj2BI4GPNOsBDgUubKqcBxzTLB/drNN8flhTX5I0AsM+g3g/8BfAI836k4D7q2p9s74W2KNZ3gO4A6D5/IGmviRpBOYNa8dJXgHcU1XXJVkyWdxStTp81r/fpcBSgAULFjAxMTGl+BbsBKftt37zFYdgqjFPp3Xr1s2IOEfF9hnM9hlsprTP0BIE8CLgqCRHAI8HnkjvjGJ+knnNWcKewJ1N/bXAXsDaJPOAXYGfbLzTqloOLAdYvHhxLVmyZErBffD8Szhr1TB//qatOWHJSL53S0xMTDDVtp0LbJ/BbJ/BZkr7DO0SU1WdXlV7VtUi4NXAl6vqBOAq4Lim2knAJc3ypc06zedfrqrHnEFIkqbHKJ6DeDPwxiSr6fUxnNuUnws8qSl/I7BsBLFJkhrTco2lqiaAiWb5NuCgljo/A46fjngkSZvnk9SSpFZblCCS7Jbkt4cVjCRpfGw2QSSZSPLEJLsD3wY+luS9ww9NkjRKXc4gdq2qB4FjgY9V1YHAS4cbliRp1LokiHlJFgKvBC4bcjySpDHRJUH8LXAFcGtVXZNkH+CW4YYlSRq1zd7mWlWfBT7bt34b8B+GGZQkafS6dFL/VpKVSW5s1n87yV8PPzRJ0ih1ucT0YeB04JcAVXUDvaEzJEmzWJcE8RtV9Y2NykYzDKokadp0SRD3Jnk6zdDbSY4D7hpqVJKkkesyFtMp9IbXfmaSHwI/AP7TUKOSJI1cl7uYbgNemmRnYLuqemj4YUmSRq3LXUzvSDK/qh6uqoea8Zj+bjqCkySNTpc+iMOr6v7Jlaq6DzhieCFJksZBlwSxfZIdJ1eS7ATsOKC+JGkW6NJJ/SlgZZKP0buT6Q3AeUONSpI0cl06qd+VZBVwGBDg7VV1xdAjkySNVKcpR6vqi8AXhxyLJGmMdLmL6dgktyR5IMmDSR5K8uB0BCdJGp0uZxDvAv6gqm4edjCSpPHR5S6mu00OkjT3dDmDuDbJZ4CLgZ9PFlbVRUOLSpI0cl0SxBOBnwIv6ysrwAQhSbNYl9tcXz8dgUiSxoszykmSWjmjnCSplTPKSZJaOaOcJKnVVGeUO2GoUUmSRm5ggkiyHbC4qpxRTpLmmIGXmKrqEeCPm+WHTQ6SNHd06YO4MsmbkuyVZPfJ19AjkySNVJc+iDc076f0lRWwz7YPR5I0Lro8Sb33VHac5PHAV+lNTzoPuLCq3ppkb2AFsDvwTeC1VfWLZlrTTwAHAj8GXlVVa6by3ZKkrbfZBJHkxLbyqvrEZjb9OXBoVa1LsgPwtSRfBN4IvK+qViT5R+Bk4Jzm/b6qekaSVwNnAq/agt8iSdqGuvRBPL/v9XvA24CjNrdR9axrVndoXgUcClzYlJ8HHNMsH82Gua4vBA5Lkg7xSZKGIFW1ZRskuwKfrKrNJokk2wPXAc8A/gF4N/D1qnpG8/lewBer6jnNWE8vr6q1zWe3Ai+oqns32udSYCnAggULDlyxYsUWxT/pnp88wN3/b0qbbrX99th1NF+8BdatW8cuu+wy6jDGlu0zmO0z2Kjb55BDDrmuqhZvrl6nOak38lNg3y4Vq+pXwP5J5gOfA57VVq15bztbeEz2qqrl9B7cY/HixbVkyZIuoTzGB8+/hLNWTeXnb701JywZyfduiYmJCabatnOB7TOY7TPYTGmfLn0Qn2fDH+rtgGcDF2zJl1TV/UkmgIOB+UnmVdV6YE/gzqbaWmAvYG2SecCuwE+25HskSdtOl/9Cv6dveT3wL5OXgQZJ8hTgl01y2Al4Kb2O56uA4+jdyXQScEmzyaXN+v9tPv9yben1L0nSNtMlQdwO3FVVPwNIslOSRR1uQV0InNf0Q2wHXFBVlyX5DrAiyd8B1wPnNvXPBT6ZZDW9MweHFJekEeqSID4L/E7f+q+asucP2qiZN+J5LeW3AQe1lP8MOL5DPJKkadDlNtd5VfWLyZVm+XHDC0mSNA66JIgfJXn0ltYkRwP3DqgvSZoFulxi+i/A+Un+vllfC7Q+XS1Jmj26jMV0K3Bwkl3oPVjnkN+SNAds9hJTknckmV9V66rqoSS7NXcgSZJmsS59EIdX1f2TK1V1H3DE8EKSJI2DLgli+2YobqD3HAS9IbwlSbNYl07qTwErk3yM3pAbb2DDqKuSpFmqSyf1u5LcQG+oDIC3V9UVww1LkjRqXYczvZ4N8zlcP7xwJEnjostdTK8EvkFvAL1XAlcnOW7YgUmSRqvLGcRfAc+vqnvg0VFa/xcbZoWTJM1CXe5i2m4yOTR+3HE7SdIM1uUM4ktJrgA+3ay/Crh8eCFJksZBl7uY/jzJscDv0psWdHlVfW7okUmSRqrTXUxVdRFw0ZBjkSSNEfsSJEmtTBCSpFabTBBJVjbvZ05fOJKkcTGoD2JhkpcARyVZQa+D+lFV9c2hRiZJGqlBCeItwDJgT+C9G31WwKHDCkqSNHqbTBBVdSFwYZL/XlVvn8aYJEljoMtzEG9PchTw4qZooqouG25YkqRR6zJY3zuBU4HvNK9TmzJJ0izW5UG5I4H9q+oRgCTn0Rvy+/RhBiZJGq2uz0HM71vedRiBSJLGS5cziHcC1ye5it6tri/GswdJmvW6dFJ/OskE8Hx6CeLNVfWvww5MkjRaXQfruwu4dMixSJLGiGMxSZJamSAkSa0GJogk2yW5cbqCkSSNj4EJonn24dtJ/s00xSNJGhNdLjEtBG5KsjLJpZOvzW2UZK8kVyW5OclNSU5tyndPcmWSW5r33ZryJDk7yeokNyQ5YOt+miRpa3S5i+lvprjv9cBpVfXNJE8ArktyJfA6YGVVnZFkGb0RY98MHA7s27xeAJzTvEuSRmCzZxBV9RVgDbBDs3wNsNm5IKrqrsk5I6rqIeBmYA/gaOC8ptp5wDHN8tHAJ6rn68D8JAu37OdIkraVLoP1/WfgQuBDTdEewMVb8iVJFgHPA64GFjTPVUw+X/HUvv3e0bfZ2qZMkjQCXS4xnQIcRO+PO1V1S5KnDt5kgyS7AP8E/FlVPZhkk1Vbyqplf0uBpQALFixgYmKiayi/ZsFOcNp+66e07daaaszTad26dTMizlGxfQazfQabKe3TJUH8vKp+MfmHPck8Wv5wt0myA73kcH5VXdQU351kYVXd1VxCuqcpXwvs1bf5nsCdG++zqpYDywEWL15cS5Ys6RLKY3zw/Es4a1WnB8m3uTUnLBnJ926JiYkJptq2c4HtM5jtM9hMaZ8udzF9JclfAjsl+X3gs8DnN7dRehnlXODmquqfsvRS4KRm+STgkr7yE5u7mQ4GHpi8FCVJmn5d/gu9DDgZWAX8EXA58JEO270IeC2wKsm3mrK/BM4ALkhyMnA7cHzz2eXAEcBq4KfA6zv+BknSEHQZzfWRZpKgq+ldWvpeVW32ElNVfY32fgWAw1rqF73+DknSGNhsgkhyJPCPwK30/uDvneSPquqLww5OkjQ6XS4xnQUcUlWrAZI8HfgCYIKQpFmsSyf1PZPJoXEbG+48kiTNUps8g0hybLN4U5LLgQvo9UEcT+9paknSLDboEtMf9C3fDbykWf4RsNvQIpIkjYVNJoiq8jZTSZrDutzFtDfwJ8Ci/vpVddTwwpIkjVqXu5gupvdE9OeBR4YbjiRpXHRJED+rqrOHHokkaax0SRAfSPJW4J+Bn08WTs71IEmanbokiP3ojal0KBsuMVWzLkmapbokiD8E9qmqXww7GEnS+OjyJPW3gfnDDkSSNF66nEEsAL6b5Bp+vQ/C21wlaRbrkiDeOvQo5phFy74wku9dc8aRI/leSTNTl/kgvjIdgUiSxkuXJ6kfYsMc1I8DdgAerqonDjMwSdJodTmDeEL/epJjgIOGFpEkaSx0uYvp11TVxfgMhCTNel0uMR3bt7odsJgNl5wkSbNUl7uY+ueFWA+sAY4eSjSSpLHRpQ/CeSEkaQ4aNOXoWwZsV1X19iHEI0kaE4POIB5uKdsZOBl4EmCCkKRZbNCUo2dNLid5AnAq8HpgBXDWpraTJM0OA/sgkuwOvBE4ATgPOKCq7puOwCRJozWoD+LdwLHAcmC/qlo3bVFJkkZu0INypwFPA/4auDPJg83roSQPTk94kqRRGdQHscVPWUuSZg+TgCSplQlCktTKBCFJamWCkCS1MkFIkloNLUEk+WiSe5Lc2Fe2e5Irk9zSvO/WlCfJ2UlWJ7khyQHDikuS1M0wzyA+Drx8o7JlwMqq2hdY2awDHA7s27yWAucMMS5JUgdDSxBV9VXgJxsVH01vyA6a92P6yj9RPV8H5idZOKzYJEmb12XCoG1pQVXdBVBVdyV5alO+B3BHX721TdldG+8gyVJ6ZxksWLCAiYmJqQWyE5y23/opbTtTbUlbrVu3bsptOxfYPoPZPoPNlPaZ7gSxKWkpa53WtKqW0xsfisWLF9eSJUum9IUfPP8Szlo1Lj9/eqw5YUnnuhMTE0y1becC22cw22ewmdI+030X092Tl46a93ua8rXAXn319gTunObYJEl9pjtBXAqc1CyfBFzSV35iczfTwcADk5eiJEmjMbRrLEk+DSwBnpxkLfBW4AzggiQnA7cDxzfVLweOAFYDP6U3MZEkaYSGliCq6jWb+OiwlroFnDKsWCRJW84nqSVJrUwQkqRWJghJUisThCSplQlCktTKBCFJamWCkCS1mluDEc1xi5Z9oXPd0/Zbz+u2oP7mrDnjyG22L0nTwzMISVIrE4QkqZUJQpLUygQhSWplgpAktTJBSJJamSAkSa1MEJKkViYISVIrE4QkqZUJQpLUygQhSWplgpAktTJBSJJamSAkSa1MEJKkVk4YpGmxJZMVbUtOVCRNnWcQkqRWJghJUisThCSplQlCktTKBCFJamWCkCS1MkFIklqZICRJrcbqQbkkLwc+AGwPfKSqzhhxSJrhhvWA3mn7red1I3r4b3N8OFDbyticQSTZHvgH4HDg2cBrkjx7tFFJ0tw1TmcQBwGrq+o2gCQrgKOB74w0KmmGcVgTbSvjlCD2AO7oW18LvGBEsUjaQv2JaZwvwY2DbdE+05GQxylBpKWsHlMpWQosbVbXJfneFL/vycC9U9x21vtT22cg22cw22ewbdE+OXOrQvjNLpXGKUGsBfbqW98TuHPjSlW1HFi+tV+W5NqqWry1+5mtbJ/BbJ/BbJ/BZkr7jE0nNXANsG+SvZM8Dng1cOmIY5KkOWtsziCqan2SPwauoHeb60er6qYRhyVJc9bYJAiAqrocuHyavm6rL1PNcrbPYLbPYLbPYDOifVL1mH5gSZLGqg9CkjRG5mSCSPLyJN9LsjrJslHHMwpJ9kpyVZKbk9yU5NSmfPckVya5pXnfrSlPkrObNrshyQGj/QXDl2T7JNcnuaxZ3zvJ1U3bfKa5mYIkOzbrq5vPF40y7umQZH6SC5N8tzmGXuixs0GS/9b8u7oxyaeTPH4mHj9zLkE4pMej1gOnVdWzgIOBU5p2WAasrKp9gZXNOvTaa9/mtRQ4Z/pDnnanAjf3rZ8JvK9pm/uAk5vyk4H7quoZwPuaerPdB4AvVdUzgefSayePHSDJHsCfAour6jn0brp5NTPx+KmqOfUCXghc0bd+OnD6qOMa9Qu4BPh94HvAwqZsIfC9ZvlDwGv66j9abza+6D2HsxI4FLiM3oOc9wLzNj6O6N1598JmeV5TL6P+DUNsmycCP9j4N3rsPPr7JkeF2L05Hi4D/v1MPH7m3BkE7UN67DGiWMZCc0r7POBqYEFV3QXQvD+1qTbX2u39wF8AjzTrTwLur6r1zXr/73+0bZrPH2jqz1b7AD8CPtZcgvtIkp3x2AGgqn4IvAe4HbiL3vFwHTPw+JmLCaLTkB5zRZJdgH8C/qyqHhxUtaVsVrZbklcA91TVdf3FLVWrw2ez0TzgAOCcqnoe8DAbLie1mVPt0/S9HA3sDTwN2JneZbaNjf3xMxcTRKchPeaCJDvQSw7nV9VFTfHdSRY2ny8E7mnK51K7vQg4KskaYAW9y0zvB+YnmXx2qP/3P9o2zee7Aj+ZzoCn2VpgbVVd3axfSC9heOz0vBT4QVX9qKp+CVwE/A4z8PiZiwnCIT3o3VkCnAvcXFXv7fvoUuCkZvkken0Tk+UnNnekHAw8MHk5YbapqtOras+qWkTv+PhyVZ0AXAUc11TbuG0m2+y4pv5Y/A9wGKrqX4E7kvzbpugwesPyz/ljp3E7cHCS32j+nU22z8w7fkbdCTKiTqQjgO8DtwJ/Nep4RtQGv0vvNPYG4FvN6wh61z5XArc077s39UPv7q9bgVX07tAY+e+YhnZaAlzWLO8DfANYDXwW2LEpf3yzvrr5fJ9Rxz0N7bI/cG1z/FwM7Oax82vt8zfAd4EbgU8CO87E48cnqSVJrebiJSZJUgcmCElSKxOEJKmVCUKS1MoEIUlqZYKQhijJUdt6xOAkb0vypmb5b5O8dFvuX5rkba5SnyTzasN4OWMpyduAdVX1nlHHotnNMwjNOkl2TvKFJN9uxuN/VVN+YJKvJLkuyRV9w0JMJHlHkq8Apyb5eJLj+va3rnlf0mx/QZLvJzkjyQlJvpFkVZKnt8TyuiR/3yx/vJkX4f8kuW3yO5IsTPLVJN9q4v29/u9tlo9L8vGW/X+8bz9rkvxNkm828Txz27Wq5iIThGajlwN3VtVzqzce/5eacac+CBxXVQcCHwX+R98286vqJVV11mb2/Vx680TsB7wW+K2qOgj4CPAnHWJbSO8p9lcAZzRl/5He0M/7N/v/VpcfuQn3VtUB9OZceNNW7Edi3uarSDPOKuA9Sc6kN0zG/07yHOA5wJW94XHYnt5QzJM+03Hf11QzjlCSW4F/7vvOQzpsf3FVPQJ8J8mCyX0CH22S2MVVtTUJYnLQxeuAY7diP5JnEJp9qur7wIH0/mi/M8lb6I0HdFNV7d+89quql/Vt9nDf8nqafxvNYGuP6/vs533Lj/StP0K3/3D1b58m3q8CLwZ+CHwyyYmTP6Wv7uM77Lt//7/qGI+0SSYIzTpJngb8tKo+RW/ilgPozWL2lCQvbOrskOTfbWIXa+glGOiN67/DkOP9TXrzT3yY3gi7k3M2353kWUm2A/5wmDFIbfwfhmaj/YB3J3kE+CXwX6vqF01n7tlJdqV37L8fuKll+w8DlyT5Br1RSR9uqbMtLQH+PMkvgXXA5BnEMnrTVd5Bb1TQXYYch/RrvM1VktTKS0ySpFYmCElSKxOEJKmVCUKS1MoEIUlqZYKQJLUyQUiSWpkgJEmt/j8g+2MqtSHxpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['serum_insulin'].hist()\n",
    "plt.xlabel('serum insulin')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['BMI'].hist()\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Diabetes_pedigree_function'].hist()\n",
    "plt.xlabel('Diabetes pedigree function')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurences')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x190668d5fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#计算各特征间的相关矩阵\n",
    "df_corr = df.corr().abs()\n",
    "plt.subplots(figsize=(16,12))\n",
    "sns.heatmap(df_corr, annot=True, cbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#数值取零无意义的特征\n",
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "df[NaN_col_names] = df[NaN_col_names].replace(0, np.NaN)\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#用中值填补NaN值\n",
    "medians = df.median() \n",
    "df = df.fillna(medians)\n",
    "\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "#数据标准化\n",
    "y_train = df['Target']\n",
    "X_train = df.drop(['Target'],axis=1)\n",
    "feat_names = X_train.columns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存为文件\n",
    "X_train = pd.DataFrame(columns=feat_names, data=X_train)\n",
    "df = pd.concat([X_train, y_train], axis=1)\n",
    "\n",
    "df.to_csv('Data_pima-indians-diabetes.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>-0.185948</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.997745</td>\n",
       "      <td>-0.818079</td>\n",
       "      <td>-0.275760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>-1.435189</td>\n",
       "      <td>-1.851862</td>\n",
       "      <td>0.329171</td>\n",
       "      <td>-0.610145</td>\n",
       "      <td>-0.211799</td>\n",
       "      <td>-0.676133</td>\n",
       "      <td>-0.616111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>-0.218823</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.414047</td>\n",
       "      <td>-1.020427</td>\n",
       "      <td>-0.360847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.547919</td>\n",
       "      <td>2.476909</td>\n",
       "      <td>-0.197433</td>\n",
       "      <td>1.808882</td>\n",
       "      <td>4.660524</td>\n",
       "      <td>-0.284572</td>\n",
       "      <td>-0.947944</td>\n",
       "      <td>1.681259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>0.109925</td>\n",
       "      <td>1.953325</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.022590</td>\n",
       "      <td>-0.724455</td>\n",
       "      <td>1.766346</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "5   0.342981                     -0.185948        0.133453   \n",
       "6  -0.250952                     -1.435189       -1.851862   \n",
       "7   1.827813                     -0.218823       -0.031990   \n",
       "8  -0.547919                      2.476909       -0.197433   \n",
       "9   1.233880                      0.109925        1.953325   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "5                    -0.012301      -0.181541 -0.997745   \n",
       "6                     0.329171      -0.610145 -0.211799   \n",
       "7                    -0.012301      -0.181541  0.414047   \n",
       "8                     1.808882       4.660524 -0.284572   \n",
       "9                    -0.012301      -0.181541 -0.022590   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  \n",
       "5                   -0.818079 -0.275760       0  \n",
       "6                   -0.676133 -0.616111       1  \n",
       "7                   -1.020427 -0.360847       0  \n",
       "8                   -0.947944  1.681259       1  \n",
       "9                   -0.724455  1.766346       1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
